The directional flow generated by peristalsis in perivascular networks -- theoretical and numerical reduced-order descriptions

TL;DR

This paper develops theoretical and numerical models to quantify how peristaltic motion in perivascular spaces can induce directional fluid flow, potentially impacting brain solute transport and clearance during different physiological states.

Contribution

It introduces reduced-order models and analytical expressions for net flow induced by peristalsis in periarterial networks, linking physiological parameters to flow outcomes.

Findings

Peristaltic motion can induce flow velocities of a few to tens of micrometers per second.

Sleep-related vasomotion increases net flow threefold.

Analytical models agree with numerical simulations for simple network configurations.

Abstract

Directional fluid flow in perivascular spaces surrounding cerebral arteries is hypothesized to play a key role in brain solute transport and clearance. While various drivers for pulsatile flow, such as cardiac or respiratory pulsations, are well quantified, the question remains as to which mechanisms could induce directional flow within physiological regimes. To address this question, we develop theoretical and numerical reduced-order models to quantify the directional (net) flow induceable by peristaltic pumping in periarterial networks. Each periarterial element is modeled as a slender annular space bounded internally by a circular tube supporting a periodic traveling (peristaltic) wave. Under the reasonable assumptions of small Reynolds number flow, small radii, and small-amplitude peristaltic waves, we use lubrication theory and regular perturbation methods to derive theoretical expressions for the directional net flow and pressure distribution in the perivascular network. The reduced model is used to derive closed-form analytical expressions for the net flow for simple network configurations of interest, including single elements, two elements in tandem, and a three element bifurcation, with results compared with numerical predictions. In particular, we provide a computable theoretical estimate of the net flow induced by peristaltic motion in perivascular networks as a function of physiological parameters, notably wave length, frequency, amplitude and perivascular dimensions. Quantifying the maximal net flow for specific physiological regimes, we find that vasomotion may induce net pial periarterial flow velocities on the order of a few to tens of mum/s and that sleep-related changes in vasomotion pulsatility may drive a threefold flow increase.